Currently,regression prediction methods based on logging data is one of the main methods for analyzing gas content of coal seams.However,the complexity of logging parameters for deep coal seams and the scarcity of mea...Currently,regression prediction methods based on logging data is one of the main methods for analyzing gas content of coal seams.However,the complexity of logging parameters for deep coal seams and the scarcity of measured gas content data signifcantly afects the accuracy and generalizability of data regression models.Accurately predicting the gas content of coal seams under small-sample condition become a difcult point in deep coalbed methane(CBM)exploration.The ModelAgnostic Meta-Learning(MAML)and Support Vector Regression(SVR)algorithms are among the few suitable for smallsample learning,exhibiting strong adaptability under limited sample conditions.In this study,logging parameters are used as input variables to construct MAML and SVR models,and their performance in predicting gas content of deep coal seams across diferent regions and layers is compared.The results demonstrate that the MAML algorithm efectively addresses the complex relationships between gas content of deep coal seam and logging parameters.The prediction errors for test dataset and new samples are merely 3.61%and 4.52%respectively,indicating exceptional adaptability,robust generalization capability,and stable model performance.In contrast,the dependency of SVR model on input parameters restricts its accuracy and generalizability in predicting gas content in deep coal seams with varying geological conditions.Although achieving a test dataset error of 4.71%,the SVR model demonstrates substantially degraded performance when applied to novel samples,with prediction errors escalating to 12.46%.Therefore,the MAML model is selected to predict gas content in the unknown areas of the Baijiahai region.The prediction results reveal that the gas content of coal seams in the Xishanyao formation(J2x)ranges from 1.32 m^(3)/t to 16.11 m^(3)/t,while that in the Badaowan Formation(J1b)varies between 1.73 m^(3)/t and 11.27 m^(3)/t.Notably,the gas enrichment areas are predominantly distributed in well blocks adjacent to fault systems,such as wells C31 and BJ8,etc.,which align with the favorable geological conditions for deep CBM accumulation in the Baijiahai region.These spatial distribution patterns not only corroborate existing geological insights but also further validate the reliability of the MAML model in predicting gas content within deep coal seams.展开更多
Deep coal reservoirs(buried depth>2000 m)represent a significant yet underexploited resource for coalbed methane(CBM)production.In these reservoirs,CBM primarily exists in adsorbed and free phase,with the pore stru...Deep coal reservoirs(buried depth>2000 m)represent a significant yet underexploited resource for coalbed methane(CBM)production.In these reservoirs,CBM primarily exists in adsorbed and free phase,with the pore structure playing a critical role in gas storage and migration.The Jiaxian block in the northeastern Ordos Basin,has emerged as a key area for deep CBM exploration due to its promising resource potential.However,the pore structure characteristics of the No.8 coal seam in Jiaxian block and their implications for gas storage and production remain poorly understood.A comprehensive characterization of the No.8 coal seam's pore structure is conducted in the study using multiple methods including high-pressure mercury injection,N2/CO_(2)adsorption experiments,and integration of measured core gas content data and production history.The study results reveal that the pores can be mainly classified as vesicles and cellular pores,and the fractures are mainly static pressure fractures.Micropores(pore diameter<10 nm)dominate the pore system(accounting for more than 99%of the total specific surface area),providing important adsorption sites for gas storage.Although mesopores(pore diameter of 100-1000 nm)and macropores(pore diameter>1000 nm)account for a small proportion,they feature effective storage spaces and interconnectivity,resulting in a high proportion of free gas.Therefore,the reservoirs shows great development potential after stimulation(such as hydraulic fracturing).These findings emphasize the feasibility of large-scale and long-term development of CBM in the Jiaxian block in terms of reservoir space,gas content and production characteristics.This study serves to lay a scientific basis for its efficient exploitation.展开更多
【目的】鄂尔多斯盆地东缘大宁−吉县区块深部煤层气已实现规模开发,投产水平井近150口,在生产过程中发现随着地层能量逐渐降低,气井携液能力下降,井筒积液成为影响深部煤层气井产量的主要因素之一。深部煤层气游离气和解吸气共同产出,...【目的】鄂尔多斯盆地东缘大宁−吉县区块深部煤层气已实现规模开发,投产水平井近150口,在生产过程中发现随着地层能量逐渐降低,气井携液能力下降,井筒积液成为影响深部煤层气井产量的主要因素之一。深部煤层气游离气和解吸气共同产出,气液比变化大,且不同阶段产气通道及排采工艺不同,适合深部煤层气水平井生产特征的积液诊断预测方法亟需建立,为积液防治提供依据,避免因积液造成储层伤害和产能影响。【方法和结果】基于不可压缩黏性流体的RANSκ-ε方程与volume of fluid method(VOF)方法,利用流体动力学软件Fluent及其二次开发功能,结合深部煤层气水平井油管、环空气液两相流物模实验,构建深部煤层气水平井气液两相流动数值模型,通过数值模拟结果建立适合于深部煤层气水平井不同井筒压力、不同井斜角、圆管条件下和环空条件下的流型图版。基于生产过程中气液两相流动规律及流型演化过程,建立流型与积液的对应关系,得出:泡状流、段塞流对应已发生积液状态,搅混流对应即将发生积液的过渡状态,环状流对应无积液或积液风险较低状态,并且井斜角大小与积液风险成正比,压力与积液风险成反比。【结论】利用积液诊断的流型图版分析法,应用于大宁−吉县区块深部煤层气水平井,指导提出干预时机,及时采取治理措施,措施有效率提高。下一步将引入人工智能技术,向智能分析预测方向进一步优化此方法,为深部煤层气井筒积液预测和防治提供技术支撑。展开更多
基金supported by the National Natural Science Foundation of China(42272200)The Science and Technology Major Project of China National Petroleum Corporation(2023ZZ18-03)+1 种基金The Science and Technology Major Project of Changqing Oilfeld(2023DZZ01)The Technology project of Huaneng Group Headquarters(Medium-deep Low-Rank Coalbed Methane Resource Potential Evaluation and Key Development Technologies of Zhalainuoer Coalfeld,HNKJ23-H51).
文摘Currently,regression prediction methods based on logging data is one of the main methods for analyzing gas content of coal seams.However,the complexity of logging parameters for deep coal seams and the scarcity of measured gas content data signifcantly afects the accuracy and generalizability of data regression models.Accurately predicting the gas content of coal seams under small-sample condition become a difcult point in deep coalbed methane(CBM)exploration.The ModelAgnostic Meta-Learning(MAML)and Support Vector Regression(SVR)algorithms are among the few suitable for smallsample learning,exhibiting strong adaptability under limited sample conditions.In this study,logging parameters are used as input variables to construct MAML and SVR models,and their performance in predicting gas content of deep coal seams across diferent regions and layers is compared.The results demonstrate that the MAML algorithm efectively addresses the complex relationships between gas content of deep coal seam and logging parameters.The prediction errors for test dataset and new samples are merely 3.61%and 4.52%respectively,indicating exceptional adaptability,robust generalization capability,and stable model performance.In contrast,the dependency of SVR model on input parameters restricts its accuracy and generalizability in predicting gas content in deep coal seams with varying geological conditions.Although achieving a test dataset error of 4.71%,the SVR model demonstrates substantially degraded performance when applied to novel samples,with prediction errors escalating to 12.46%.Therefore,the MAML model is selected to predict gas content in the unknown areas of the Baijiahai region.The prediction results reveal that the gas content of coal seams in the Xishanyao formation(J2x)ranges from 1.32 m^(3)/t to 16.11 m^(3)/t,while that in the Badaowan Formation(J1b)varies between 1.73 m^(3)/t and 11.27 m^(3)/t.Notably,the gas enrichment areas are predominantly distributed in well blocks adjacent to fault systems,such as wells C31 and BJ8,etc.,which align with the favorable geological conditions for deep CBM accumulation in the Baijiahai region.These spatial distribution patterns not only corroborate existing geological insights but also further validate the reliability of the MAML model in predicting gas content within deep coal seams.
基金funded by the National Key R&D Program of China(2024YFC2909400)the National Natural Science Foundation of China(42402180,42202195)the tackling applied science and technology projects of China National Petroleum Corporation(2023ZZ18)。
文摘Deep coal reservoirs(buried depth>2000 m)represent a significant yet underexploited resource for coalbed methane(CBM)production.In these reservoirs,CBM primarily exists in adsorbed and free phase,with the pore structure playing a critical role in gas storage and migration.The Jiaxian block in the northeastern Ordos Basin,has emerged as a key area for deep CBM exploration due to its promising resource potential.However,the pore structure characteristics of the No.8 coal seam in Jiaxian block and their implications for gas storage and production remain poorly understood.A comprehensive characterization of the No.8 coal seam's pore structure is conducted in the study using multiple methods including high-pressure mercury injection,N2/CO_(2)adsorption experiments,and integration of measured core gas content data and production history.The study results reveal that the pores can be mainly classified as vesicles and cellular pores,and the fractures are mainly static pressure fractures.Micropores(pore diameter<10 nm)dominate the pore system(accounting for more than 99%of the total specific surface area),providing important adsorption sites for gas storage.Although mesopores(pore diameter of 100-1000 nm)and macropores(pore diameter>1000 nm)account for a small proportion,they feature effective storage spaces and interconnectivity,resulting in a high proportion of free gas.Therefore,the reservoirs shows great development potential after stimulation(such as hydraulic fracturing).These findings emphasize the feasibility of large-scale and long-term development of CBM in the Jiaxian block in terms of reservoir space,gas content and production characteristics.This study serves to lay a scientific basis for its efficient exploitation.
文摘【目的】鄂尔多斯盆地东缘大宁−吉县区块深部煤层气已实现规模开发,投产水平井近150口,在生产过程中发现随着地层能量逐渐降低,气井携液能力下降,井筒积液成为影响深部煤层气井产量的主要因素之一。深部煤层气游离气和解吸气共同产出,气液比变化大,且不同阶段产气通道及排采工艺不同,适合深部煤层气水平井生产特征的积液诊断预测方法亟需建立,为积液防治提供依据,避免因积液造成储层伤害和产能影响。【方法和结果】基于不可压缩黏性流体的RANSκ-ε方程与volume of fluid method(VOF)方法,利用流体动力学软件Fluent及其二次开发功能,结合深部煤层气水平井油管、环空气液两相流物模实验,构建深部煤层气水平井气液两相流动数值模型,通过数值模拟结果建立适合于深部煤层气水平井不同井筒压力、不同井斜角、圆管条件下和环空条件下的流型图版。基于生产过程中气液两相流动规律及流型演化过程,建立流型与积液的对应关系,得出:泡状流、段塞流对应已发生积液状态,搅混流对应即将发生积液的过渡状态,环状流对应无积液或积液风险较低状态,并且井斜角大小与积液风险成正比,压力与积液风险成反比。【结论】利用积液诊断的流型图版分析法,应用于大宁−吉县区块深部煤层气水平井,指导提出干预时机,及时采取治理措施,措施有效率提高。下一步将引入人工智能技术,向智能分析预测方向进一步优化此方法,为深部煤层气井筒积液预测和防治提供技术支撑。
